library(tidyverse)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(vegan)

Read in and Prep-process data

Read in Taxa Tables

find samples with 100% unknown and remove them

samples.unknown <- species_table %>% 
  melt () %>% 
  group_by(variable) %>% 
  summarise(sum=sum(value)) %>% 
  filter(sum == 0) %>% 
  pluck(1) %>% 
  as.vector()
No id variables; using all as measure variables
# Remove samples with no known taxa
species_table <- species_table[, -which(names(species_table) %in% samples.unknown)] 

Read in mapping table

remove neg controls; set sample.type factor

species_table <- species_table[,rownames(map_table)] # reorder to match metadata
species_table <- species_table[,-c(grep("Control", map_table$sample.type))] # remove control samples

genus_table <- genus_table[,rownames(map_table)]
genus_table <- genus_table[,-c(grep("Control", map_table$sample.type))]

map_table <- map_table[-c(grep("Control", map_table$sample.type)), ]
map_table$sample.type = factor(map_table$sample.type)

all(row.names(map_table) %in% colnames(species_table))
[1] TRUE

optional, remove follow up BAL

# species_table <- species_table[,rownames(map_table)] # rereorder to match metadata

# species_table <- species_table[,-c(grep("FALSE", map_table$is.baseline))]

#map_table <- map_table[-c(grep("FALSE", map_table$is.baseline)), ]

# set sample type as factor

#all(row.names(map_table) %in% colnames(species_table)) # Sanity check

Alpha diversity by sample type

compute shannon and simpson diversity metrics

diversity_vec = matrix(nrow = dim(species_table)[2], ncol = 2)
diversity_vec = as.data.frame(diversity_vec)
for (a in 1:dim(species_table)[2]) {
  diversity_vec[a,1] = diversity(species_table[,a], index = "shannon")
  diversity_vec[a,2] = diversity(species_table[,a], index = "simpson")
}
colnames(diversity_vec) = c("Shannon", "Simpson")

# add sample.type factor
diversity_vec$sample.type = map_table$sample.type
diversity_vec$sample.type = factor(diversity_vec$sample.type) # Optional: Add Levels

boxplots

ggplot(diversity_vec, aes(x = sample.type, y = Shannon, fill = sample.type)) +
  geom_boxplot(outlier.shape = NA, alpha = 0.7) +
  #geom_violin(alpha = 0.8) +
  geom_jitter(size = 1, width = 0.1, alpha = 0.35) +
  #scale_fill_manual(values=c("blue", "lightgreen", "yellow", "brown", "violet", "gray"),
  #                  labels = c("Toothbrush", "HMP-Oral", "HMP-Skin", "HMP-Gut", "HMP-Vaginal", "Building dust")) +
  theme_bw() +
  theme(legend.position = "none",
        axis.title.x = element_blank(),
        #axis.text.x = element_blank(), 
        axis.ticks.x = element_blank(), 
        axis.title.y = element_text(size = 14),
        axis.text.y = element_text(size = 14))

ANOVA stats

summary(aov(Shannon ~ sample.type, diversity_vec))
             Df Sum Sq Mean Sq F value Pr(>F)
sample.type   2   0.96  0.4790   0.896  0.409
Residuals   223 119.14  0.5343               
TukeyHSD(aov(Shannon ~ sample.type, diversity_vec))
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = Shannon ~ sample.type, data = diversity_vec)

$sample.type
                              diff        lwr       upr     p adj
Pneumonia-NonPneumonia -0.01538685 -0.4412036 0.4104299 0.9960005
Unknown-NonPneumonia    0.20087248 -0.3418635 0.7436084 0.6577194
Unknown-Pneumonia       0.21625932 -0.1650512 0.5975698 0.3755754

Beta diversity between sample types

# beta-diversity measure
beta <- vegdist(t(species_table), 'bray', binary = T)
beta.mat <- as.matrix(beta)

# projection
pcoa <- cmdscale(beta, k = 4, eig = TRUE)

# cleanup
ord <- as.data.frame(pcoa$points)
names(ord) <- c('pcoa1', 'pcoa2', 'pcoa3', 'pcoa4') ## rename coordinates

# add metadata
ord$Category = map_table$sample.type
ord$Outcome = map_table$binned_outcome
ord$Baseline = map_table$is.baseline
ord$Diagnosis = map_table$diagnosis.subtype

# Percent explained variation
eig <- eigenvals(pcoa)
eig.percent <- 100*head(eig/sum(eig))
eig.percent
[1] 27.568102 10.935027  9.314035  8.172572  5.591935  4.743136
eig.1 <- paste("PCo1 (", as.character(round(eig.percent[1], digits = 1)), "%)", sep = "")
eig.2 <-paste("PCo2 (", as.character(round(eig.percent[2], digits = 1)), "%)", sep = "")
## plot PCoA (FIGURE 1A)
ggplot(data = ord, aes(x = pcoa1, y = pcoa2, fill = Diagnosis)) +
  geom_point(size = 3, stroke = 1, shape=21) +
  theme_bw() +
  xlab(eig.1) +
  ylab(eig.2) +
  theme_q2r() +
  theme(axis.text = element_text(size=15, color = "black"),
        axis.title = element_text(size=16, color = "black"),
        legend.text = element_text(size=15, color = "black"),
        legend.title = element_text(size=15, color = "black")) +
  scale_fill_brewer(guide="legend", palette="Set1") +
  #scale_shape_manual(values=c(21))+#, 22, 23, 24, 25, 26)) +
  guides(fill=guide_legend(override.aes=list(shape=21))) +
   theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) 

ggsave("../../results/notebook_out/02A_MetaphlanPCoA.pdf", units="in", width=7, height=4.5)
Error in grDevices::pdf(file = filename, ..., version = version) : 
  cannot open file '../../results/notebook_out/02A_MetaphlanPCoA.pdf'

PERMANOVA Stats

# effect of sample type
beta <- vegdist(t(species_table), 'bray', binary = T)
adonis_out <- adonis2(beta ~ sample.type, data = map_table, permutations = 999)
adonis_out
Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 999

adonis2(formula = beta ~ sample.type, data = map_table, permutations = 999)
             Df SumOfSqs      R2      F Pr(>F)  
sample.type   2    1.364 0.01712 1.9416  0.017 *
Residual    223   78.308 0.98288                
Total       225   79.672 1.00000                
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Distance between samples

sample.type.1 <- map_table$sample.type[1]
sample.type.1
[1] Pneumonia
Levels: NonPneumonia Pneumonia Unknown
mean(as.matrix(vegdist(t(species_table[,grep(sample.type.1,map_table$sample.type)]), 
                       'jaccard', binary = T)))
[1] 0.8587207
sd(as.matrix(vegdist(t(species_table[,grep(sample.type.1,map_table$sample.type)]), 
                     'jaccard', binary = T)))/sqrt(34)
[1] 0.03923536

Heat Map Most Abundant Taxa

Get most abundant, prevalent species in ranked order

min_mean_proportion <- .00001
min_prevalence <- 20

species.ranked <- species_table %>% 
  rownames_to_column() %>% 
  melt() %>% 
  filter(value > 0) %>% # filter 0 values to get prevalence
  group_by(rowname) %>%
  summarise(mean = mean(value), n = n(), sum=sum(value)) %>% 
  arrange(desc(mean)) %>% 
  #filter(mean > min_mean_proportion) %>% # Filter by mean percent
  filter(n > min_prevalence) %>% # Filter by prevalence
  pluck(1)
Using rowname as id variables
length(species.ranked)
[1] 19

Order by species rank object and remove extra taxnomic info

species_table.heat <- species_table[species.ranked,] %>% 
  rownames_to_column() %>%
  separate(rowname, into = c("ExtraTaxa", "Taxa"), sep="s__") %>%
  select(!ExtraTaxa) %>% 
  column_to_rownames("Taxa") %>% 
  as.matrix
library(pheatmap)
annot.col <- map_table %>% select(sample.type, binned_outcome, is.baseline) 

paletteLength <- 100

myColors <- rev(colorRampPalette(rev(brewer.pal(n = 9, name ="Reds")))(paletteLength))
myColors <- rev(colorRampPalette(rev(brewer.pal(n = 9, name ="RdBu")))(paletteLength))

#pdf(file="../../results/notebook_out/02A_MetaphlanHeatPlot.pdf", width = 25, height = 20)
pheatmap(species_table.heat, 
         color = myColors,
         annotation_col = annot.col,
         angle_col = "45",
         show_colnames=FALSE,
         show_rownames = FALSE, scale="row")

#dev.off()

Genus

min_mean_proportion <- .00001
min_prevalence <- 20

genera.ranked <- genus_table %>% 
  rownames_to_column() %>% 
  melt() %>% 
  filter(value > 0) %>% # filter 0 values to get prevalence
  group_by(rowname) %>%
  summarise(mean = mean(value), n = n(), sum=sum(value)) %>% 
  arrange(desc(mean)) %>% 
  #filter(mean > min_mean_proportion) %>% # Filter by mean percent
  filter(n > min_prevalence) %>% # Filter by prevalence
  pluck(1)
Using rowname as id variables
length(genera.ranked)
[1] 13
# Order by species rank object and remove extra taxnomic info
genus_table.heat <- genus_table[genera.ranked,] %>% 
  rownames_to_column() %>%
  separate(rowname, into = c("ExtraTaxa", "Taxa"), sep="g__") %>%
  select(!ExtraTaxa) %>% 
  column_to_rownames("Taxa") %>% 
  as.matrix

Testing as phyloseq + Playground

---
title: "02A_MetaPhlanTaxonomicAnalysis"
output: html_notebook
---

```{r}
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(vegan)

```

# Read in and Prep-process data
## Read in Taxa Tables
```{r}

taxa_table = read_table('../../results/metaphlan_bowtie_out/merged_metaphlan_profile.tsv', skip = 1) %>%
                        select(!NCBI_tax_id)

# Remove .metaphlan_profile from ends of files 
colnames(taxa_table) <- sapply(strsplit(names(taxa_table), ".metaphlan_profile"), `[[`, 1)

genus_table <- taxa_table %>% filter(grepl("g__", clade_name), !grepl("s__", clade_name))
genus_table <- genus_table %>% column_to_rownames(var = c("clade_name"))

species_table <- taxa_table %>% filter(grepl("s__", clade_name))
species_table <- species_table %>% column_to_rownames(var = c("clade_name"))
```
# find samples with 100% unknown and remove them
```{r}
samples.unknown <- species_table %>% 
  melt () %>% 
  group_by(variable) %>% 
  summarise(sum=sum(value)) %>% 
  filter(sum == 0) %>% 
  pluck(1) %>% 
  as.vector()

# Remove samples with no known taxa
species_table <- species_table[, -which(names(species_table) %in% samples.unknown)] 

```

## Read in mapping table
```{r}
map_table <- read_table('../../config/map_table.tsv') %>%
                        #filter(!sample %in% samples.unknown) %>% # remove samples with unkown
                        column_to_rownames(var = c("sample"))
map_table <- map_table[-which(rownames(map_table) %in% samples.unknown),] 

```


## remove neg controls; set sample.type factor
```{r}
species_table <- species_table[,rownames(map_table)] # reorder to match metadata
species_table <- species_table[,-c(grep("Control", map_table$sample.type))] # remove control samples

genus_table <- genus_table[,rownames(map_table)]
genus_table <- genus_table[,-c(grep("Control", map_table$sample.type))]

map_table <- map_table[-c(grep("Control", map_table$sample.type)), ]
map_table$sample.type = factor(map_table$sample.type)

all(row.names(map_table) %in% colnames(species_table))

```

## optional, remove follow up BAL
```{r}
# species_table <- species_table[,rownames(map_table)] # rereorder to match metadata

# species_table <- species_table[,-c(grep("FALSE", map_table$is.baseline))]

#map_table <- map_table[-c(grep("FALSE", map_table$is.baseline)), ]

# set sample type as factor

#all(row.names(map_table) %in% colnames(species_table)) # Sanity check

```



####################################
# Alpha diversity by sample type 
####################################

## compute shannon and simpson diversity metrics
```{r}
diversity_vec = matrix(nrow = dim(species_table)[2], ncol = 2)
diversity_vec = as.data.frame(diversity_vec)
for (a in 1:dim(species_table)[2]) {
  diversity_vec[a,1] = diversity(species_table[,a], index = "shannon")
  diversity_vec[a,2] = diversity(species_table[,a], index = "simpson")
}
colnames(diversity_vec) = c("Shannon", "Simpson")

# add sample.type factor
diversity_vec$sample.type = map_table$sample.type
diversity_vec$sample.type = factor(diversity_vec$sample.type) # Optional: Add Levels
```


## boxplots 
```{r}
ggplot(diversity_vec, aes(x = sample.type, y = Shannon, fill = sample.type)) +
  geom_boxplot(outlier.shape = NA, alpha = 0.7) +
  #geom_violin(alpha = 0.8) +
  geom_jitter(size = 1, width = 0.1, alpha = 0.35) +
  #scale_fill_manual(values=c("blue", "lightgreen", "yellow", "brown", "violet", "gray"),
  #                  labels = c("Toothbrush", "HMP-Oral", "HMP-Skin", "HMP-Gut", "HMP-Vaginal", "Building dust")) +
  theme_bw() +
  theme(legend.position = "none",
        axis.title.x = element_blank(),
        #axis.text.x = element_blank(), 
        axis.ticks.x = element_blank(), 
        axis.title.y = element_text(size = 14),
        axis.text.y = element_text(size = 14))
```
## ANOVA stats
```{r}
summary(aov(Shannon ~ sample.type, diversity_vec))
TukeyHSD(aov(Shannon ~ sample.type, diversity_vec))
```
####################################
# Beta diversity between sample types 
####################################

```{r}
# beta-diversity measure
beta <- vegdist(t(species_table), 'bray', binary = T)
beta.mat <- as.matrix(beta)

# projection
pcoa <- cmdscale(beta, k = 4, eig = TRUE)

# cleanup
ord <- as.data.frame(pcoa$points)
names(ord) <- c('pcoa1', 'pcoa2', 'pcoa3', 'pcoa4') ## rename coordinates

# add metadata
ord$Category = map_table$sample.type
ord$Outcome = map_table$binned_outcome
ord$Baseline = map_table$is.baseline
ord$Diagnosis = map_table$diagnosis.subtype

# Percent explained variation
eig <- eigenvals(pcoa)
eig.percent <- 100*head(eig/sum(eig))
eig.percent
eig.1 <- paste("PCo1 (", as.character(round(eig.percent[1], digits = 1)), "%)", sep = "")
eig.2 <-paste("PCo2 (", as.character(round(eig.percent[2], digits = 1)), "%)", sep = "")

```

```{r}
## plot PCoA (FIGURE 1A)
ggplot(data = ord, aes(x = pcoa1, y = pcoa2, fill = Diagnosis)) +
  geom_point(size = 3, stroke = 1, shape=21) +
  theme_bw() +
  xlab(eig.1) +
  ylab(eig.2) +
  theme_q2r() +
  theme(axis.text = element_text(size=15, color = "black"),
        axis.title = element_text(size=16, color = "black"),
        legend.text = element_text(size=15, color = "black"),
        legend.title = element_text(size=15, color = "black")) +
  scale_fill_brewer(guide="legend", palette="Set1") +
  #scale_shape_manual(values=c(21))+#, 22, 23, 24, 25, 26)) +
  guides(fill=guide_legend(override.aes=list(shape=21))) +
   theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) 
ggsave("../../results/notebook_out/02A_MetaphlanPCoA.pdf", units="in", width=7, height=4.5)
```

## PERMANOVA Stats
```{r}
# effect of sample type
beta <- vegdist(t(species_table), 'bray', binary = T)
adonis_out <- adonis2(beta ~ sample.type, data = map_table, permutations = 999)
adonis_out
```

## Distance between samples
```{r}
sample.type.1 <- map_table$sample.type[1]
sample.type.1

mean(as.matrix(vegdist(t(species_table[,grep(sample.type.1,map_table$sample.type)]), 
                       'jaccard', binary = T)))
sd(as.matrix(vegdist(t(species_table[,grep(sample.type.1,map_table$sample.type)]), 
                     'jaccard', binary = T)))/sqrt(34)
```

####################################
# Heat Map Most Abundant Taxa
####################################

## Get most abundant, prevalent species in ranked order
```{r}
min_mean_proportion <- .00001
min_prevalence <- 20

species.ranked <- species_table %>% 
  rownames_to_column() %>% 
  melt() %>% 
  filter(value > 0) %>% # filter 0 values to get prevalence
  group_by(rowname) %>%
  summarise(mean = mean(value), n = n(), sum=sum(value)) %>% 
  arrange(desc(mean)) %>% 
  #filter(mean > min_mean_proportion) %>% # Filter by mean percent
  filter(n > min_prevalence) %>% # Filter by prevalence
  pluck(1)

length(species.ranked)
```

## Order by species rank object and remove extra taxnomic info
```{r}
species_table.heat <- species_table[species.ranked,] %>% 
  rownames_to_column() %>%
  separate(rowname, into = c("ExtraTaxa", "Taxa"), sep="s__") %>%
  select(!ExtraTaxa) %>% 
  column_to_rownames("Taxa") %>% 
  as.matrix
```

```{r}
library(pheatmap)
annot.col <- map_table %>% select(sample.type, binned_outcome, is.baseline) 

paletteLength <- 100

myColors <- rev(colorRampPalette(rev(brewer.pal(n = 9, name ="Reds")))(paletteLength))
myColors <- rev(colorRampPalette(rev(brewer.pal(n = 9, name ="RdBu")))(paletteLength))

#pdf(file="../../results/notebook_out/02A_MetaphlanHeatPlot.pdf", width = 25, height = 20)
pheatmap(species_table.heat, 
         color = myColors,
         annotation_col = annot.col,
         angle_col = "45",
         show_colnames=FALSE,
         show_rownames = FALSE, scale="row")
#dev.off()
```


## Genus
```{r}
min_mean_proportion <- .00001
min_prevalence <- 20

genera.ranked <- genus_table %>% 
  rownames_to_column() %>% 
  melt() %>% 
  filter(value > 0) %>% # filter 0 values to get prevalence
  group_by(rowname) %>%
  summarise(mean = mean(value), n = n(), sum=sum(value)) %>% 
  arrange(desc(mean)) %>% 
  #filter(mean > min_mean_proportion) %>% # Filter by mean percent
  filter(n > min_prevalence) %>% # Filter by prevalence
  pluck(1)

length(genera.ranked)
```
```{r}
# Order by species rank object and remove extra taxnomic info
genus_table.heat <- genus_table[genera.ranked,] %>% 
  rownames_to_column() %>%
  separate(rowname, into = c("ExtraTaxa", "Taxa"), sep="g__") %>%
  select(!ExtraTaxa) %>% 
  column_to_rownames("Taxa") %>% 
  as.matrix
```

```{r}
library(pheatmap)
annot.col <- map_table %>% select(sample.type, binned_outcome, is.baseline) 

paletteLength <- 50

myColors <- rev(colorRampPalette(rev(brewer.pal(n = 9, name ="Reds")))(paletteLength))
myColors <- rev(colorRampPalette(rev(brewer.pal(n = 9, name ="RdBu")))(paletteLength))

#pdf(file="../../results/notebook_out/02A_MetaphlanHeatPlotGenera.pdf", width = 25, height = 20)
pheatmap(genus_table.heat, 
         color = myColors,
         annotation_col = annot.col,
         angle_col = "45",
         show_colnames=FALSE,
         show_rownames = FALSE, scale="row")
#dev.off()
```


####################################
# Testing as phyloseq + Playground
####################################

```{r}
library(tidyverse); packageVersion("tidyverse")     #version:1.3.0 
library(phyloseq); packageVersion("phyloseq")       #version:1.32.0


#Building MetaPhlAn species abundance ps object
s_abund <- read_tsv("../../results/metaphlan_bowtie_out/merged_metaphlan_profile_all.tsv") %>% select(!NCBI_tax_id)

s_tax_tab <- s_abund %>%
  dplyr::rename("taxonomy" = "clade_name") %>%
  dplyr::select(taxonomy) %>%
  tidyr::separate(taxonomy, into = c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"), sep = "\\|") %>%
  dplyr::mutate(spec_row = Species) %>%
  tibble::column_to_rownames(var = "spec_row")

s_otu_tab <- s_abund %>%
  dplyr::rename("taxonomy" = "clade_name") %>%
  tidyr::separate(taxonomy, into = c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species"), sep = "\\|") %>%
  dplyr::select(-Kingdom, -Phylum, -Class, -Order, -Family, -Genus) %>%
  tibble::column_to_rownames(var = "Species")

names(s_otu_tab) <- gsub(names(s_otu_tab), pattern = ".metaphlan_profile", replacement = "") 

head(colSums(s_otu_tab))
s_otu_tab <- s_otu_tab / 100                                                   #convert to proportion with unit sum of 1
head(colSums(s_otu_tab))

s_meta <- data.frame(seq_id = names(s_otu_tab))
s_meta <- s_meta %>%
  dplyr::mutate(sampleNames_row = seq_id) %>%
  tibble::column_to_rownames(var = "sampleNames_row")

(ps_mpa3_species <- phyloseq(sample_data(s_meta),
                             otu_table(s_otu_tab, taxa_are_rows = TRUE),
                             tax_table(as.matrix(s_tax_tab))))
library(vegan)
decostand(s_otu_tab, method="normalize", MARGIN=1)
```
